In a manufacturing environment, equipment is operated to produce a product. It is important to detect when an equipment process has drifted, is no longer on target and is producing products that do not meet specifications.
The process can then be corrected such that the equipment is back on target. One method of accomplishing this control is statistical quality control. Statistical quality control comprises measuring some of the specifications of a product and plotting those measurements on a chart. The measurements are plotted run after run of the equipment. When a statistically significant variation is identified, some correction to the equipment is made. Rules for determining when a statistically significant deviation occurs have been created. The most common rules in use are the WECO rules which allow an operator to make measurements, put the measurements on a graph, and then take an appropriate action.
Statistical process control is an advancement upon statistical quality control. Statistical process control comprises judging measurements against the most recent measurement norm, instead of a fixed target. In other words, measured values are tracked versus the difference from a normal value rather than in terms of the actual measured value. Within the area of statistical process control, model based statistical process control has been developed.
In model based statistical process control, a relationship is defined between measurable output of the equipment and the control settings and other inputs to the equipment. The quality of the product being produced can be related to the status of the equipment. The relationship can be empirical or a theoretical model.
In general, a model based statistical process control unit uses a model of the equipment process. The control unit re-tunes the model by changing one or more model coefficients to shift the mean of the distribution back to center any time the equipment drifts away from where it should be. A minimum and maximum value can be set for each coefficient to keep re-tuning by the control unit from driving the equipment too far away from expected normal operating values.
In a model based statistical process control unit, a relationship is established between product measurables and the equipment control settings used. This relationship or model can be tuned by changing one or more coefficients that define the model. As equipment variations occur, the actual measured data could fall below or rise above an expected value predicted by the model for given control settings. A drift away from the expected value can be detected. The model based statistical process control unit can then re-calculate and determine new model coefficients for the equipment control settings in order to tune the model to the current operating characteristics of the equipment. A minimum and maximum model coefficient can be defined to prevent drifting too far from a normal process. If the coefficient passes these limits, the operation of the equipment can be shut down to determine why the equipment has drifted so far off baseline norms.
With such model based statistical process control units, a tuning event comprising a coefficient shift, occurs only when a statistically valid drift of equipment parameters has occurred. Thus, the information that is provided by the coefficient shifts is variation from mean that is statistically significant. A coefficient can be plotted against time to see the stability of a piece of equipment in a valid form regardless the time period viewed.
However, model based statistical process control suffers from a number of problems. There is no way to know from coefficient shift information from a piece of equipment whether the model re-tuning is caused by variables associated with the equipment, by the manufacturing environment, or by some other source. In order to make such a determination, it is necessary to gather large amounts of information from other information sources and spend time analyzing possible causes. It is difficult for an engineer or operator using equipment having a model based statistical process control unit to efficiently diagnose problems caused by environmental change as opposed to equipment specific problems.